The Challenge

A rapidly growing fintech client reached out to Sentrix with a common but critical problem: Their AWS bill was skyrocketing, and they didn't know why.

As a startup, they had prioritized speed of development over cost efficiency. This is a valid strategy for the early days—getting to market is everything. But as they scaled to 500,000 users, the technical debt began to compound into varying degrees of financial debt. Their monthly AWS bill had crossed $45,000 and was growing by 15% month-over-month, threatening their burn rate.

The Audit: Finding the Leaks

We conducted a comprehensive 3-day audit of their infrastructure using trusted tools and our proprietary scripts. We looked for "zombie" resources—things that were running but doing nothing.

Key Findings:

1. Over-provisioned Databases: Their RDS instances were sized for a "Black Friday" traffic peak that only happened once a year. They were running at 5% CPU utilization 99% of the time.
2. Forgotten Dev Environments: Developers woud spin up expensive EC2 instances for testing and forget to shut them down. We found 12 m5.2xlarge instances that hadn't accepted a connection in 3 weeks.
3. Data Transfer Costs: Their architecture was chatting across Availability Zones (AZs) unnecessarily, incurring massive data transfer fees.

The Solution: A Three-Pronged Strategy

We didn't just want to patch the leak; we wanted to fix the plumbing.

1. Right-sizing & Modernization

• Databases: We migrated their RDS instances to Aurora Serverless v2. This allowed the database to scale down to almost zero during the night when traffic was low, and scale up instantly during trading hours.
• Compute: We analyzed CPU/RAM usage and downsized production instances. We moved reliable, stateless workloads to Graviton (ARM) processors, which offer a 20% price/performance improvement instantly.

2. Spot Instances for Background Jobs

The client had a massive "reports generation" worker fleet that ran on On-Demand instances.

• Action: We migrated these workers to Spot Instances. Since these jobs were fault-tolerant (if a server dies, the job just retries), Spot instances were perfect.
• Result: ~70% reduction in compute costs for this specific workload.

3. Lifecycle Policies

They were storing terabytes of application logs in standard S3 storage forever.

• Action: We implemented S3 Intelligent-Tiering and lifecycle policies. Logs older than 30 days were moved to Glacier Instant Retrieval, and logs older than 1 year were deleted (per their compliance policy).
• Result: Storage costs dropped by 60%.

The Results

Within 30 days of implementation, the results were stark:

• 32% reduction in total monthly AWS spend (saving ~$14,000/month).
• Zero downtime during the optimization process.
• Improved Performance: The move to Graviton processors actually sped up their API response times by 10%.

The Cultural Shift

More importantly, we helped them implement FinOps practices. We set up AWS Budgets and Slack alerts. Now, if a dev environment's cost spikes by 50% in a day, the engineering manager gets a Slack notification immediately, not at the end of the month when the invoice arrives.

"Sentrix didn't just save us money; they taught us how to be efficient. The savings paid for their engagement fee in 2 months." — CTO, Fintech Client

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Our Cloud Services

This case study showcases our Cloud Solutions expertise. We help enterprises migrate, optimize, and manage their cloud infrastructure. Combined with our Managed Services, we provide 24/7 monitoring to catch cost anomalies before they become problems.

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